Decision trees -- Inbenta uses decision trees to define the flow of
conversation the system will take. At every step of the tree, the
Inbenta chatbot may ask clarifying questions to better understand the
situation. If a user asks a question with great detail and enough
context, the chatbot can make a direct match with a deep node of the
decision tree;

Transactional intelligence -- The Inbenta chatbot uses Webhooks, an
HTTP callback, to integrate business systems such as billing, CRM and
inventory; to complete customer transactions; or to answer questions.
Webhooks also can be used to create decision trees to gather additional
information. When used as JavaScript callbacks, they can support
encrypted connections.

Content Digest -- A newly designed Content Digest feature integrated
with Inbenta's natural language search engine lets the chatbot pull
information from diverse sources such as an enterprise's website, its
knowledge base, or its technical documentation.

Seamless escalation -- Inbenta's intelligence detects cues, or
triggers, during the customer interaction, which signals to the chatbot
when to turn the conversation over to a human agent, and selects the
appropriate escalation path in real time.

Familiar Approaches

The tools may be new to users of Inbenta's tech, but they are hardly innovations.

Webhooks, for example, have been around for about a decade, noted Holger Mueller, a principal analyst at Constellation Research.

"Nothing really new here," said Rebecca Wettemann, vice president of research at Nucleus Research.

"From Inbenta, it's going to be important to hear about time to
value, speed of integration, and the ability to leverage outside data to
improve chatbots dynamically over time," she told CRM Buyer.

Although the announcements aren't groundbreaking, they're "solid
blocking and tackling -- a good way to integrate with third-party
systems," Mueller told CRM Buyer.

What Works for Inbenta

The key to what has been working for Inbenta "is an NLP engine that
grows over time and, more importantly, the fact that answers are
contextual," observed Ray Wang, principal analyst at Constellation
Research.

"In this area of chatbots and expert systems, infinite ambient orchestration is key," he noted.

The chatbot's ability to work automatically with back-end systems is a plus, Wang told CRM Buyer.

Further, the integration of Inbenta's natural language search engine
with its Content Digest "is not easy to do," he pointed out, and it's
"something only a handful of vendors, including Inbenta, can do."

Seamless escalation is another plus because "most systems have static
rules and triggers," Wang said. "It appears Inbenta's doing this in a
variable manner."

Chatbots work well to answer Level 1 and 2 questions, he said. More
complex scenarios still must be routed to humans, but they "must be
tagged so the system learns why they were routed and how that resolution
was addressed."

The Need for AI Chatbots

Interest in custom AI chatbots has been growing, suggests a survey of
530 European and North American companies Spiceworks conducted last
month. Among its findings:

3 percent of organizations with more than 500 employees had implemented custom-built AI chatbots;